hve-data-science
Exploratory Data Analysis (EDA), Bronze/Silver/Gold DataOps tiering, DS_CATALOG_V1 conventions, and MVE experiment design.
Skill by UnicoLab
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/unicolab-hve-data-science
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: design, data
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/hve-data-science
What's inside
- Skill name
hve-data-science- Description
- Exploratory Data Analysis (EDA), Bronze/Silver/Gold DataOps tiering, DS_CATALOG_V1 conventions, and MVE experiment design.
- License
- MIT
- Instructions
- 13 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# HVE Data Science, DataOps & Experiment Design ## 1. Minimum Viable Experiment (`experiment-designer-planner`) - State a falsifiable hypothesis, baseline benchmark, primary success metric, and guard-rail metrics (latency, memory, regression ceiling). - Define dataset splits, deterministic random seeds, and ablation variables before running experiments. ## 2. DataOps Tiering & `DS_CATALOG_V1` (`data-science-worker`) - **Bronze (Raw)**: Immutable ingested payloads with schema/timestamp provenance; never mutate raw inputs in place. - **Silver (Cleaned / Validated)**: Deduplicated, schema-validated, null-handled records with explicit data-quality assertions. - **Gold (Feature / Evaluation Aggregates)**: Deterministic feature tables, evaluation summaries, and reproducible metric artifacts. - Catalog datasets and transformations with clear input/output schemas, row-count invariants, and lineage notes.
From UnicoLab/smlcode, MIT.
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Signals
- Stars
- 1
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 20, 2026 (9 days ago)
- Last release
- Oct 20, 2026 (9 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/UnicoLab/smlcode
- Website
- unicolab.github.io/smlcode/
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
hve-data-science skill. Agent Mart. https://agentmart.dev/skills/unicolab-hve-data-science (last updated 2026-10-24)